Implementing Natural Language Cognitive Architecture with GPT-3 and the "nexus" concept

David Shapiro · Intermediate ·🧠 Large Language Models ·4y ago

Key Takeaways

The video demonstrates implementing natural language cognitive architecture with GPT-3 and the nexus concept, showcasing a shared file system for memory and various services for audio and video processing.

Full Transcript

hey everyone david shapiro here uh i just have a really quick update i wanted to share an insight that i had about how to implement raven or natural language cognitive architecture um basically what i what i realized was um because raven's gonna run as an instance probably in a container i can actually use a shared file system for a lot of the memory and so what i mean by that is well let me just show you so i unfortunately i can't have the services running because i'm using the audio services of my computer but basically what what it does is the microphone will capture a sequence of audio files which can then be used for inferencing things like speaker recognition um speech to text that sort of thing and it'll be cached here and then used uh and then it'll be cleaned up so it'll just be a buffer and then it'll be translated into normal text here so you see um whoops there it is so you see uh it's speech-to-text uh and that's that's that's basically kind of the gist of it there's still a lot of services set up uh here's me drinking a beer so this is basically what raven sees and that will be translated into text via inference image based inference and so that's what i'm working on now is setting up these services then there's another thing so that's the audio cache and the video cache so that's basically the sensory buffer for an agi next is the heartbeat so you know how you can be aware of your own body it's called proprioception this is basically raven's proprioception the every service that runs is going to create a heartbeat file here and every every time every time that one of those files cycles it will update the the time stamp so this way raven will be aware of the services that are running there's a whole bunch of other services that i need to work on i don't want to spoil anything but they're coming so microphone and camera that's the time stamp that they were running last here's the services you can see it's uh what just 64 lines of python some of it is comments so these are these are very small services they just use file they use files to share and then what i'm going to set up next is the is the speech to text service and then a video inference or image object detection inference the advantage of having it set up this way let me go back to the file folder sorry the advantage to having it set up this way is that any number an arbitrary number of audio processing services can use these files so let's say in the long run i'm going to have music recognition ambient sound recognition emotional tone recognition there's going to be all kinds of services using these audio files to generate inferences and you see the fact that every audio file has a time stamp attached to it that means that raven will know exactly when he heard what so that's pretty cool and same with the video again there's me drinking a beer as i'm getting set up so right now the uh the frame rate for this is one frame per second or actually it's every two seconds um that's because it's expensive to run this stuff um and it would be excessive to run it at any higher rate in the future raven will be updating the vision at you know like one frame per second two frames per second ten frames per second and eventually you know as the technology gets cheaper and faster raven will see as fast as you or i do that might be five or ten years down the road and then the memories so everything that enters raven's consciousness the agis consciousness will end up as a log file here eventually i'm going to transfer this into a private encrypted blockchain because they're they're chronologically sequential everything that raven thinks sees hears says everything is going to end up here in the memories directory and then it's going to be encrypted and put in a blockchain for privacy's sake but for now i'm just using log files i'm thinking about using something like syslog there's a there's a technology called the elk stack which is elasticsearch log stash and kibana which is a tool for capturing and visualizing logs because that's essentially what the content of raven's consciousness is is a series of log files it's that simple and i know all of this looks really simple it's not complete yet so keep that in mind and it is you might think of it as elegantly simple but i also think of it as deceptively simple because just because something is complex or next generation right that doesn't mean that it's that it's uh that it has to be complex or or over the top right simple solutions are are often the best solutions so anyways that's it i just wanted to post a quick update about this thanks for watching bye

Original Description

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Playlist

Uploads from David Shapiro · David Shapiro · 8 of 60

1 Raven MVP Demo 2021-04-02
Raven MVP Demo 2021-04-02
David Shapiro
2 Get Started with Raven AGI
Get Started with Raven AGI
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3 Coding Raven's Encyclopedia Service (v.1)
Coding Raven's Encyclopedia Service (v.1)
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4 Prototype AGI demo - Natural Language Cognitive Architecture "NLCA" running on GPT-3
Prototype AGI demo - Natural Language Cognitive Architecture "NLCA" running on GPT-3
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5 Raven Release 1 Deep Dive
Raven Release 1 Deep Dive
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6 Fine-tuning GPT-3 to generate questions about anything
Fine-tuning GPT-3 to generate questions about anything
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7 Fine-tuning GPT-3 for benevolent and trustworthy AGI
Fine-tuning GPT-3 for benevolent and trustworthy AGI
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Implementing Natural Language Cognitive Architecture with GPT-3 and the "nexus" concept
Implementing Natural Language Cognitive Architecture with GPT-3 and the "nexus" concept
David Shapiro
9 5 Tips and Misconceptions about Finetuning GPT-3
5 Tips and Misconceptions about Finetuning GPT-3
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10 How to create synthetic datasets with GPT-3
How to create synthetic datasets with GPT-3
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11 What is a heuristic imperative? What imperatives should we give AGI?
What is a heuristic imperative? What imperatives should we give AGI?
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12 Talking Philosophy with GPT-3
Talking Philosophy with GPT-3
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13 Talking Boundaries and Consent with GPT-3
Talking Boundaries and Consent with GPT-3
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14 Convergence and acceleration towards AGI (or Artificial Cognitive Entities)
Convergence and acceleration towards AGI (or Artificial Cognitive Entities)
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15 GPT-3 for Writing Dialog
GPT-3 for Writing Dialog
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16 Co-writing flash fiction with GPT-3
Co-writing flash fiction with GPT-3
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17 From zero to finetuned model in 1 hour with GPT-3. Generate a movie script from any premise!
From zero to finetuned model in 1 hour with GPT-3. Generate a movie script from any premise!
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18 GPT-3 Working Session: Finetune an information companion chatbot in 30 minutes (RESEARCH ONLY)
GPT-3 Working Session: Finetune an information companion chatbot in 30 minutes (RESEARCH ONLY)
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19 What is "toxic stoicism"? Talking philosophy with GPT-3
What is "toxic stoicism"? Talking philosophy with GPT-3
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20 Billion-dollar GPT-3 startup! Fix education with an expert tutor chatbot!
Billion-dollar GPT-3 startup! Fix education with an expert tutor chatbot!
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21 Finetune GPT-3 to write an entire coherent novel (part 1)
Finetune GPT-3 to write an entire coherent novel (part 1)
David Shapiro
22 Concepts in Neuroscience and Cognition - Deficits of GPT-3 and the path to AGI and ACE
Concepts in Neuroscience and Cognition - Deficits of GPT-3 and the path to AGI and ACE
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23 Finetuning GPT-3 to be a master tutor that can handle any topic and hostile students
Finetuning GPT-3 to be a master tutor that can handle any topic and hostile students
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24 Testing "Theory of Mind" in GPT-3 - making fully aligned ACOG (Artificial Cognitive Entities)
Testing "Theory of Mind" in GPT-3 - making fully aligned ACOG (Artificial Cognitive Entities)
David Shapiro
25 Finetune GPT-3 to write an entire coherent novel (part 2)
Finetune GPT-3 to write an entire coherent novel (part 2)
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26 Finetune multiple cognitive tasks with GPT-3 on medical texts (and reduce hallucination)
Finetune multiple cognitive tasks with GPT-3 on medical texts (and reduce hallucination)
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27 Finetune GPT-3 to write a novel - Part 3 (IT WORKS!!!) ...at least a little bit
Finetune GPT-3 to write a novel - Part 3 (IT WORKS!!!) ...at least a little bit
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28 How will we know when we've invented AGI? How will we know it is complete?
How will we know when we've invented AGI? How will we know it is complete?
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29 Finetuning a Creative Writing Coach in GPT-3 - Part 1
Finetuning a Creative Writing Coach in GPT-3 - Part 1
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30 Finetune GPT-3 to write a coherent novel - Part 4 (success! with minor bugs...)
Finetune GPT-3 to write a coherent novel - Part 4 (success! with minor bugs...)
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31 Recursively summarize text of any length with GPT-3
Recursively summarize text of any length with GPT-3
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32 Finetuning a Creative Writing Coach in GPT-3 - Part 2
Finetuning a Creative Writing Coach in GPT-3 - Part 2
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33 Increasingly Verbose Bot with GPT-3 - Expand any word or phrase into a whole paragraph
Increasingly Verbose Bot with GPT-3 - Expand any word or phrase into a whole paragraph
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34 Metaprompting with GPT-3 to dynamically generate arguments
Metaprompting with GPT-3 to dynamically generate arguments
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35 I'm taking a short break from research and YouTube
I'm taking a short break from research and YouTube
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36 Are LaMDA or GPT-3 sentient? No, but...
Are LaMDA or GPT-3 sentient? No, but...
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37 Can GPT-3 generate training data? Short answer? Yes! Here's why that's a legit methodology...
Can GPT-3 generate training data? Short answer? Yes! Here's why that's a legit methodology...
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38 DALLE2 Style Tags Tutorial - "Elven archer in a sunny forest" with different tags
DALLE2 Style Tags Tutorial - "Elven archer in a sunny forest" with different tags
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39 Many of you have asked for it: Join my new research Discord! Link in description
Many of you have asked for it: Join my new research Discord! Link in description
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40 Answer complex questions from an arbitrarily large set of documents with vector search and GPT-3
Answer complex questions from an arbitrarily large set of documents with vector search and GPT-3
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41 Fixing "goldfish memory" with GPT-3 and external sources of information in a chatbot - part 1
Fixing "goldfish memory" with GPT-3 and external sources of information in a chatbot - part 1
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42 Fixing "goldfish memory" with GPT-3 and external sources of information in a chatbot - part 2
Fixing "goldfish memory" with GPT-3 and external sources of information in a chatbot - part 2
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43 Python & GPT-3 for Absolute Beginners #1 - Setting up your environment
Python & GPT-3 for Absolute Beginners #1 - Setting up your environment
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44 Python & GPT-3 for Absolute Beginners #2 - Your first chatbot
Python & GPT-3 for Absolute Beginners #2 - Your first chatbot
David Shapiro
45 Python & GPT-3 for Absolute Beginners #3 - What the heck are embeddings?
Python & GPT-3 for Absolute Beginners #3 - What the heck are embeddings?
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46 Introducing the RAVEN MVP - a general purpose AI companion (with a live DEMO)
Introducing the RAVEN MVP - a general purpose AI companion (with a live DEMO)
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47 I needed SQLITE but for vectors so I wrote it myself. Now it's on PyPI - introducing VDBLITE
I needed SQLITE but for vectors so I wrote it myself. Now it's on PyPI - introducing VDBLITE
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48 Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting
Prompt Engineering 101: Autocomplete, Zero-shot, One-shot, and Few-shot prompting
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49 Prompt Engineering 101: Introduction to CODEX
Prompt Engineering 101: Introduction to CODEX
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50 Prompt Engineering 101: Summarizing, Extraction, and Rewriting
Prompt Engineering 101: Summarizing, Extraction, and Rewriting
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51 Summarize product reviews with GPT-3 fast and easy, get product insights and improvements fast!
Summarize product reviews with GPT-3 fast and easy, get product insights and improvements fast!
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52 Finetuning GPT-3 101: Synthesizing Training Data
Finetuning GPT-3 101: Synthesizing Training Data
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53 Finetuning GPT-3 101: Augmenting Training Data
Finetuning GPT-3 101: Augmenting Training Data
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54 Finetuning GPT-3 101: Using Your Finetuned Model
Finetuning GPT-3 101: Using Your Finetuned Model
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55 Modeling different viewpoints with GPT-3 for automatic debates
Modeling different viewpoints with GPT-3 for automatic debates
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56 Finetune a perfect email generator in GPT-3 - take any input and generate a great email
Finetune a perfect email generator in GPT-3 - take any input and generate a great email
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57 Research Update: Nexus microservice for Artificial Cognition + microservices architecture (MARAGI)
Research Update: Nexus microservice for Artificial Cognition + microservices architecture (MARAGI)
David Shapiro
58 Research Update: Microservices! Text-based simulation, Embeddings, and Nexus
Research Update: Microservices! Text-based simulation, Embeddings, and Nexus
David Shapiro
59 It's alive! The first 3 microservices are up and running!
It's alive! The first 3 microservices are up and running!
David Shapiro
60 What is a Microservice? What does it have to do with AGI?
What is a Microservice? What does it have to do with AGI?
David Shapiro

The video teaches how to implement natural language cognitive architecture with GPT-3 and the nexus concept, using a shared file system for memory and various services for audio and video processing. This approach enables the development of Artificial General Intelligence (AGI) with capabilities such as speech-to-text, image object detection, and proprioception. The video provides a practical example of how to design and implement such a system.

Key Takeaways
  1. Set up a shared file system for memory
  2. Develop services for audio processing
  3. Develop services for video processing
  4. Implement speech-to-text and image object detection
  5. Use a heartbeat file for proprioception
  6. Consider using the ELK stack or syslog for log management
  7. Plan for encryption and blockchain-based storage for privacy
💡 Simple solutions can be effective for complex problems, and a shared file system can be used to enable multiple services to access and process audio and video data.

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